Understanding spatial variability in longevity

One of the notable findings from analysis of longevity data is extreme spatial heterogeneity. Looking at variations in life expectancy at small scales is startling (see map below). As the linked article notes, there are two census tracts that are less than ten miles apart in Washington DC yet have average life expectancy differing by thirty three years. While this example is an extreme, fine-scale mapping shows many case of large differences in longevity between nearby regions.

There are strong correlations between wealth, education, and life expectancy (see Fig 13 in the linked paper for a summary) and there are obvious spatial clusters of wealthy, well-educated people with high life expectancy and of those who are poor, have little education, and don’t live very long. What can we learn from this data? As I have discussed in another post, the wealth-life expectancy correlation does not appear to be explained by access to health care in studies from other countries. In the U.S.over the past two decades, however, healthcare spending for higher-income families has surged past that for middle- and lower-income families.

It is also well-documented that poorer people face much larger exposure to pollution and this exposure obviously has a significant spatial component. Poorer households are more likely to live near major roads and tend to be closer to polluting industries.

One of the questions in linking geography and longevity relates to obesity, which is a factor in a range of longevity-related health problems. There are clear large-scale spatial patterns in obesity. Is there a link between obesity and wealth that might help to explain the smaller-scale variability in life expectancy? The relationship between obesity and wealth in the U.S. is not entirely clear. The linked report finds that “the prevalence of obesity decreased with increasing income in women (from 45.2% to 29.7%), but there was no difference in obesity prevalence between the lowest (31.5%) and highest (32.6%) income groups among men.” The study also concludes, however, that “the age-adjusted prevalence of obesity among adults was lower in the highest income group (31.2%) than the other groups (40.8%)…. The age-adjusted prevalence of obesity among college graduates was lower (27.8%) than among those with some college (40.6%) and those who were high school graduates or less (40.0%).”

My conclusion from reading about localized geographic variations in longevity is that we are simply observing the outcomes when wealthier, better-educated people increasingly self-select to live together in desirable locations. These enclaves of privilege tend to have good public schools and much greater resources overall. Lower-income families are excluded by the high costs of housing.As documented in Charles Murray’s Coming Apart, Americans used to live in neighborhoods that were far less economically segregated than they are today. Highly-localized spatial heterogeneity in longevity reflects increasing inequality and socioeconomic segregation.

The really important question is where we go with this. Would moving people out of the distressed neighborhoods improve their outcomes? Data suggest that moving people to “better” areas can have a big impact on their lives in a number of areas, although these studies do not have enough history to reach conclusions with regard to longevity. While we may be able to improve things with low-income housing subsidies in high-cost neighborhoods, the map shown at the beginning of this article also highlights the larger-scale regional disparities in longevity. The nine states with the shortest life expectancy are all in the South. The state-to-state variability in longevity is harder to address.